Data transfer for network interaction fraudulence detection

ABSTRACT

Transferring metadata is disclosed. Information about a network interaction is processed to generate metadata describing the network interaction. Based on the metadata it is determined whether the metadata is to be transferred to an aggregator. In the event that the metadata is to be transferred, one or more aggregators are determined to which the metadata is to be transferred. The metadata is transferred to the one or more aggregators.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of copending U.S. patent applicationSer. No. 12/859,150, filed Aug. 18, 2010 (allowed), which is acontinuation of copending U.S. patent application Ser. No. 11/986,312,filed Nov. 19, 2007, now U.S. Pat. No. 7,801,985 issued Sep. 21, 2010,which claims priority to U.S. Provisional Application No. 61/190,066filed Mar. 22, 2007, which applications are incorporated herein byreference in their entireties.

BACKGROUND OF THE INVENTION

Network interaction fraudulence detection is typically performed bylogging information from a plurality of locations and processing all thelogged information from the plurality of locations at a processinglocation. Detection is improved by having logged information from anumber of locations because information can be aggregated across all thelocations. However, as the number of locations increases, the processinglocation must process an increasing amount of logged information. Thiswill act as a bottleneck in the processing of logged information andultimately impede scaling detection. systems. It would be beneficial tobe able to aggregate across locations without having a bottleneck in theprocessing for fraudulence so that detection systems can scale.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a block diagram illustrating an embodiment of a system fornetwork interaction fraudulence detection.

FIG. 2A is a block diagram illustrating an embodiment of tieredaggregation for network interactions.

FIG. 2B is a block diagram illustrating an embodiment of tieredaggregation of network interactions.

FIG. 3 is a block diagram illustrating an embodiment of a peer systemmonitoring network interactions.

FIG. 4 is a block diagram illustrating an embodiment of a peer.

FIG. 5 is a block diagram illustrating an embodiment of an aggregator.

FIG. 6 is a flow diagram illustrating an embodiment of a process fordata transfer for network interaction fraudulence detection.

FIG. 7A is a flow diagram illustrating an embodiment of a process forprocessing information about a network interaction.

FIG. 7B is a flow diagram illustrating an embodiment of a process forprocessing information about a network interaction.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess, an apparatus, a system, a composition of matter, a computerreadable medium such as a computer readable storage medium or a computernetwork wherein program instructions are sent over optical orcommunication links. In this specification, these implementations, orany other form that the invention may take, may be referred to astechniques. A component such as a processor or a memory described asbeing configured to perform a task includes both a general componentthat is temporarily configured to perform the task at a given time or aspecific component that is manufactured to perform the task. In general,the order of the steps of disclosed processes may be altered within thescope of the invention.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

Data transfer for network interaction fraudulence detection isdisclosed. Network interaction fraudulence detection becomes moreaccurate as more information regarding network interactions (e.g.,clicks) is analyzed. In particular, correlations of aggregated networkinteraction information and the outcomes of each network interaction canbe used to assess fraudulence or legitimacy of a given networkinteraction. Although more information is usually better, scaling thehandling of more and more information is problematic. Effective andefficient use of network interaction information requires determinationof what information to aggregate and where and how to aggregate it.

In some embodiments, determining the information or metadata to shareregarding a given network interaction includes determining a confidencelevel or certainty level based on a model or combination of models whichprocess information regarding a plurality of network interactions.Metadata can then be shared in the event that a confidence level isbelow a threshold, a certainty level is above a threshold and theprocessing indicates that the given network interaction is bad, acertainty level is above a threshold and the processing indicates thatthe given network interaction is good, a certainty is very low, a redalert is issued, a very bad site is identified, a random sampling isshared and/or any other appropriate manner of determining whether toshare information.

A model inputs information from a layer about the given networkinteraction and determines that, for example, it is 70% certain that theIP address is an address that is the source of fraudulent networkinteraction activity. For example, the model may determine thiscertainty by using factors such as layer 3 network interactioninformation that shows that the source internet protocol (IP) addresshas been involved with spam and is not available for tracing. The modeltakes in factors and using a weighted total of the factors determines acertainty. In another example, the model determines this certainty byusing factors such as layer 7 network information that shows that asession user visits 4 different sites where the user never converted,clicked on different products but where the spacing of the clicks hasexactly the same timing (i.e., indicating an automatic pattern ofaccess). The model takes in factors and has predetermined valuesassociated with conditions. The maximum value of any of these conditionsis then used to determine a certainty.

In various embodiments, an aggregator comprises a centralized locationin order to be able to broadcast it to all or some other locations, alocal aggregator, a regional aggregator, a country aggregator, one ormore layer or tier aggregators, an organizational aggregator, a segmentaggregator, and/or among one or more peers. In some cases, sharing mayneed to follow a set of rules, contractual obligations, may need to befiltered, be based on network topology, system network architecture,and/or any other appropriate sharing criteria.

The data or information about a network interaction to share can includejust an IP address and a time stamp or include one or more of thefollowing: an IP address, a time stamp, assessment of whether the IPaddress is a source of fraudulent network interactions, referrerinformation, metadata indicating confidence, measured information suchas interclick time, processed information such as model parameters,interclick distributions, click statistics, HTTP data TCP data, machinefingerprint data, and/or user fingerprint data. In some embodiments,information is determined by protocol—for example, all networkinteraction information regarding fraudulent sources is sent to aparticular port or IP address whereas all legitimate sources are sent toa different port or IP address. In some cases, business rules or logicdetermine the information to share—for example, if a keyword is involvedin the network interaction and the keyword has a value associated withit (e.g., a dollar amount), then a predetermined list of information isshared with a predetermined set of aggregators. In some cases,information to be shared is derived automatically as determined bymonitoring a network interaction or as gleaned by probing the networkbased on information determined from monitoring. In some cases,information to be shared is derived manually. For example, model anddata about network interactions are shared except when the networkinteraction involves a particular site (e.g., a military site orgovernmental site).

In some embodiments, rule-based determinations regarding whatinformation to share and who to share it with may have conflictingdeterminations. In these cases, a conflict resolving set of rules orprioritization methodology for the rule, or a manual intervention (e.g.,manual determination of which rule is to “win”) of configuration fileinput may be required.

FIG. 1 is a block diagram illustrating an embodiment of a system fornetwork interaction fraudulence detection. In the example shown, peer114, which measures or analyzes a single location's networkinteractions, is able to communicate with network 100. Peer 114comprises a computer system or server that receives or measures networkinteractions and is able to analyze (e.g., perform statistical measuresof network interactions, actively probe information regarding thenetwork interaction, model, parameterize, compress, etc.) andcommunicate (e.g., exchange information with a remote analytical system,a remote aggregator of network interactions, a remote storagerepository, etc.) regarding the measured network interactions. Network100 enables communication to one or more aggregators of the networkinteraction information including local aggregator 102, organizationalaggregator 104, segment aggregator 106, centralized aggregator 108,country aggregator 110, and/or regional aggregator 112.

Local aggregator 102 receives and analyzes network interactions in alocality. In various embodiments, a locality comprises a local networkor systems attached to the local network, where the local network isassociated with one of the following: a company, a physical plant, abuilding, a neighborhood, an area, a wireless hub, a wired hub, a routeror switch, or any other constraint for a network or systems attached toa network.

Organizational aggregator 104 receives and analyzes network interactionsof an organization. In various embodiments, an organization comprises acomputer network or systems attached to the computer network, where theorganization comprises one or more of the following: a company includingone or more locations that can be geographically disparate, agovernment, a union, a non-profit organization, a university, anassociation, a partnership, or any other group of member entities wherethe member entities are associated with each other.

Segment aggregator 106 receives and analyzes network interactions of asegment. In various embodiments, a segment comprises a computer networkor system attached to the computer network, where the segment comprisesa group of companies, governmental departments, and/or otherorganizations/entities associated with a type of activity. For example,a group of advertisers, three advertising networks, a set of computercompanies, a dozen telephone companies located in different countries,stock brokerages, insurance companies, etc.

Centralized aggregator 108 receives and analyzes network interactionsfrom appliances monitoring network interactions. Appliances or analyzingsystems or servers can be associated with local organizational, segment,country or regional aggregators as well as only centralized aggregator108. In some embodiments, there are several tiers/layers/levels ofaggregation.

Country aggregator 110 and regional aggregator 112 are geographicallyorganized aggregators. In some cases, network interactions may aggregateat a local level; a plurality of local aggregators 102 are aggregated ata regional level; a plurality of regional aggregators 112 are aggregatedat a country level; and a plurality of country aggregators 110 areaggregated at a centralized level.

In various embodiments, network 100 comprises one or more of thefollowing: the Internet, the world wide web, a local area network, awide area network, a wired network, a fiber network, a wireless network,or any other appropriate network enabling communication between systemsanalyzing, monitoring, and/or storing with network interactions.

In various embodiments, only one of the type of aggregators, a subset ofthe types of aggregators, all of the types of aggregators, or more thandisplayed types of aggregators included in FIG. 1 aggregate networkinteractions.

FIG. 2A is a block diagram illustrating an embodiment of tieredaggregation for network interactions. In the example shown, networkinteractions from peer 1, peer 2, and peer 3 are aggregated by local 1aggregator. Network interactions from peer A, peer B, and peer C areaggregated by local 2 aggregator. Network interactions from local 1aggregator and local 2 aggregator are aggregated by region M aggregator.Network interactions from region 1 aggregator through region Maggregator are aggregated by country 1 aggregator. M region aggregatorsare represented in FIG. 2A by region 1 aggregator and region Maggregator. Network interactions from country 1 aggregator throughcountry N aggregator are aggregated by centralized aggregator. N countryaggregators are represented in FIG. 2A by country 1 aggregator andcountry N aggregator.

FIG. 2B is a block diagram illustrating an embodiment of tieredaggregation of network interactions. In the example shown, layer tierserver aggregates network interactions from local tier peer 1, localtier peer 2, local tier peer 3, and local tier peer 4. Local tiernetwork interactions are also aggregated or shared by the peers in thelocal tier. So, each local tier peer receives network interactioninformation from other local tier peers—for example, local tier peer 1receives network interaction information from local tier peer 2, localtier peer 3, and local tier peer 4.

Information about network interactions including processed data, modeldata and model parameters is processed by peers and/or aggregatorsincluding filtering, prioritizing, ranking, discarding, or any otherappropriate processing in order to be useful in determining networkinteraction fraudulence. In some cases, the transferring of informationabout network interactions may not be successful—for example, ifconnections and/or hardware resources are busy or unavailable. Transfersmay need to be resent after a period of time or different periods oftime depending on how many retries have beer attempted (e.g.,anti-entropy back off methods to enable graceful scaling of sharing),sharing for aggregation may require transferring information

FIG. 3 is a block diagram illustrating an embodiment of a peer systemmonitoring network interactions. In some embodiments, peer 308 of FIG. 3is the same as peer 114 of FIG. 1 and/or peers in FIGS. 2A and 2B. Inthe example shown, a user using computer 312 can access a web page onserver 306 via network 300. In various embodiments, server 306 isassociated with an advertising network or an advertiser. In variousembodiments, network 300 comprises one or more of the following: theInternet, the world wide web, a local area network, a wide area network,a wired network, a wireless network, or any other appropriate network.Server 306 can be accessed from network 300 via firewall 302 and localarea network (LAN) 304. Peer 308 is able to monitor traffic to and fromserver 306 and is connected to LAN 304. In various embodiments,monitoring comprises detecting in hardware the network traffic or thenetwork interactions to be monitored, detecting in real-time networktraffic, capturing data in real-time, analyzing data in real-time,triggering real-time queries or forensics of IP addresses/networktopology/routing tables/preferred paths, detecting layer 3 through layer7 data from the monitored traffic, monitoring Ethernet traffic, or anyother appropriate monitoring of network traffic. Peer 308 is able tostore information on storage device 310. In some embodiments peer 308monitors traffic to and from server 306 by being between server 306 andLAN 304 by receiving and forwarding all traffic between network 300 andserver 306. In this situation, all traffic is received and forwardedwithout substantially affecting network traffic, without substantiallyaffecting a transaction involving the network traffic, and/or with lessthan 2 milliseconds of delay for the process of receiving andforwarding.

In some embodiments, peers can also be used to monitor traffic at otherpoints in the network other than in front of or just beside a server—forexample, on a trunk line, an Internet service provider network, anadvertising network, or any other appropriate traffic site.

In some embodiments, peer 308 is able to communicate with a server thatanalyzes aggregated network interaction information and/or providesmodel data based on network interaction data provided by peer 308.Models are used by peer 308 to calculate a preliminary score inreal-time or quasi-real-time for detected network interactions. Apreliminary score can be based on information associated with detectednetwork interaction(s) as well as on stored parameters or modelsreceived from a model server or an analytics server.

FIG. 4 is a block diagram illustrating an embodiment of a peer. In someembodiments, peer 400 of FIG. 4 the same as peer 308 of FIG. 3, Peer 114of FIG. 1 and/or peers in FIGS. 2A and 2B. In the example shown, peer400 includes monitor 402, analyzer 404, and database 406. Monitor 402receives information regarding network interactions and/or networktraffic and sends information to analyzer 404. Monitor 402 monitorsinformation in real-time and Analyzer 404 can provide real-time orquasi-real-time assessment of information gathered by monitor 402. Insome embodiments, monitor 402 receives information in real-time frompeer hardware that is detecting the network traffic to be monitored.Analyzer 404 analyzes network interactions and/or network traffic byexamining TCP/IP or hypertext transfer protocol/secure hypertexttransfer protocol (HTTP/HTTPS) layer information and uses that to make apreliminary scoring of the network interaction. Analyzer 404 can performinterpret protocol (IP) forensics including probing the internet orworld wide web to discover information about web sites and/or webrouters or other networking components. Analyzer 404 can also sendnetwork interaction information and/or model information or modelparameter information to one or more other peers and/or to one or moreaggregators.

Preliminary scoring is based on models generated by analyzer 404 and/orreceived from a model server and/or an analytic server, and on otherpreviously acquired network interaction information that is storedlocally. Some models are specific in terms of scoring a networkinteraction—for example, a specific IP address is designated as scoringhigh because it was recently involved in a successful financialtransaction or as scoring low because it was recently involved insending significant amounts of spam and phishing email. Some models aregeneric in terms of scoring a network interaction—for example, an IPaddress that cannot be located with a probe immediately after beingreceived in a click is designated as scoring low because it is likelyfraudulent. Local peer stored history can indicate that a large numberof network interactions are coming from one particular IP address in ashort period of time; Analyzer 404 can indicate that under thesecircumstances that these network interactions are designated with a lowpreliminary score. Models used in preliminary scoring of monitorednetwork interactions can be updated locally by updating model parametersusing local peer network interaction data, updated from remote modeland/or analytic servers either with new models or new model parameters,or any other appropriate manner of updating models for preliminaryscoring.

Preliminary scoring or other analysis preformed by analyzer 404 can bereal-time or in batch mode after a period of time. In some embodiments,monitoring and/or detection of a network interaction is done inreal-time by peer hardware. Extraction and capture of relevant networkinteraction information (e.g., layer 3 through layer 7 information) foranalysis is performed in real-time. In some embodiments, preliminaryscoring is provided to a user in real-time or quasi-real-time.

In various embodiments, the network interaction comprises a click, acookie, or any other appropriate network interaction. Analyzer 404 isable to probe M real time the IP routing and perform forensics. Analyzer404 can store network interaction and/or network traffic information indatabase 206. Analyzer 204 can send network interaction reports to amodel server and also can receive from a model server fraud modeldigests. An analytic server can calculate a final score for the networkinteraction based on information received at the analytic server. Insome embodiments, the final score is sent to a report server or areporter on an aggregator, from which a customer can access summarizedlegitimate and fraudulent network interactions. In some embodiments,analysis and model generation is performed on peer machines such as peer400 within analyzer 404 instead of a separate analysis server.

FIG. 5 is a block diagram illustrating an embodiment of an aggregator.In some embodiments, aggregator 500 of FIG. 5 is used to implementaggregators of FIGS. 1 and/or 2A and/or layer tier server of FIG. 2B. Inthe example shown, aggregator 500 includes modeler 502, analyzer 504,database 506, and reporter 508. Aggregator 500 communicates with otheraggregators and/or one or more peers and aggregates networkinteractions. Aggregator receives network interaction information anduses the information to analyze network interactions in analyzer 504 andmodel network interactions in modeler 502. In various embodiments, areceiver of network information may accept, prioritize, rank, refuse, orany other appropriate processing or filtering as may be appropriate.Modeler 502 can develop models and parameter values for the models ofnetwork interactions which can be used to analyze network interactions.In some embodiments, models and parameter values are manually input intomodeler 502 using a configuration file or another input method such as ainteractive user interface. In some embodiments, models are notautomatically developed in modeler 502. Analyzer 504 can analyze networkinteractions that have been aggregated from one or more monitoring peersover a period of time. In various embodiments, analysis of networkinteractions includes statistical analysis; analysis using modelsgenerated and parameterized by model 502; network topology/routeanalysis (e.g., analyzing if there are common sites or routes being usedfor network interactions); timing analysis (e.g., analysis of networkinteraction events from a site or a user at a site); calculating aconfidence level, a certainty level, a red alert, model parameters,interclick times, interclick distributions, statistics regarding clicks,composite measure of clicks; or any other appropriate analysis. Database506 is used to store relevant network interaction information, analysisinformation, and model information. Reporter 508 is able to createreports regarding network interactions, analysis, and/or models based onnetwork interaction information, model information, and analysisinformation including information stored in database 506.

FIG. 6 is a flow diagram illustrating an embodiment of a process fordata transfer for network interaction fraudulence detection. In someembodiments, the process of FIG. 6 is executed on a peer such as peer114 of FIG. 1, peer or local tier peer of FIGS. 2A and 2B, peer 308 ofFIG. 3, and/or peer 400 of FIG. 4. In the example shown, in 600information is processed about a network interaction to calculate adata. In 602, it is determined if the data is to be transferred. If thedata is not to be transferred, the process ends. If the data is to betransferred, then in 604, receiver(s) is/are determined to which thedata is to be transferred. In 606, data is transferred to thereceiver(s).

In some embodiments, information about a network interaction, comprisesHTTP data, TCP data machine fingerprint data, or user fingerprint data

HTTP data includes date, time, client IP address, user name, method,uniform resource identifier (URI) stem, URI query, protocol status,bytes sent, user agent, referrer, and/or cookie.

TCP data includes TCP packet info such as source port, destination port,sequence number, acknowledgement number, data offset, control flags,window, checksum, urgent pointer, options, padding, and/or data.

Machine fingerprinting data includes all information that javascript hasaccess to including machine hardware characteristics, browser settings,browser environment (bookmarks, history), and browser customization(plugins that have been registered), and/or timing information betweenthe client and other machines.

In some embodiments, machine fingerprinting data includes language andlocale information such as browserLanguage, systemLanguage, UserLanguage, defaultCharset, Locale Observes daylight savings time (DST),Currently DST, Standard Timezone Offset, Locale Date Format.

In some embodiments, machine fingerprinting data includes machineproperty information such as operating system and/or central processingunit class (oscpu/cpuClass), screen x dots per inch (XDPI), screen ydots per inch (YDPI), screen fontSmoothingEnabled, screenupdateInterval, platform, Screen Width, Screen Height, and/or ProcessingTime msec.

In some embodiments, machine fingerprint data includes sessioninformation such as domain and/or start time in msec.

In some embodiments, machine fingerprint data includes browser propertyinformation such as appCodeName, appName, appVersion,productSub/appMinorVersion, userAgent, cookieEnabled, and/or online,

In some embodiments, machine fingerprint data includes component versioninformation such as interact explorer™ (IE), IE javascript™ (JS) MajorVersion, IE JS Minor Version, IE JS Build Version, Outlook Express™Address Book Version, Windows™ Desktop Update, DirectAnimation™,DirectAnimation Java™ Classes, DirectShow™ activemovie, Dynamic HTMLData Binding Tridata, DirectAnimation Java Classes DAJava™, InternetConnection Wizard, Internet Explorer 6, Java(Sun™) JAVAVM™, InternetExplorer Help, HTML Help, Microsoft™ Windows Media Player, NetMeeting™,Offline Browsing Pack, Microsoft Outlook Express 6 MailNews™, TaskScheduler, and/or Java(Sun) JAVAVM.

In some embodiments, machine fingerprinting data includes FireFox™information such as Acrobat™, Flash™, QuickTime™, Java Plug-in,Director™, and/or Office™.

User fingerprint data includes capturing all user keystrokes and mousemovements between HTTP page requests including timing of the keystrokesand mouse movements at millisecond resolution.

FIG. 7A is a flow diagram illustrating an embodiment of a process forprocessing information about a network interaction. In some embodiments,the process of FIG. 7A is used to implement 600 of FIG. 6. In theexample shown, in 700 information is received about a networkinteraction. In 702, the information is processed using model(s) tocalculate a confidence or certainty level.

FIG. 7B is a flow diagram illustrating an embodiment of a process forprocessing information about a network interaction. In some embodiments,the process of FIG. 7B is used to implement 600 of FIG. 6. In theexample shown, in 700 information is received about a networkinteraction. In 702, the information is processed using model(s) tocalculate a red alert.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A system for acquiring and aggregating data fornetwork fraudulence detection, the system comprising: a plurality ofpeers, each of the plurality of peers comprising an electronic processorand a storage device, and each of the plurality of peers beingconfigured to: receive network interaction data from hardware configuredto detect network traffic, determine, based on one or more models, ascore for the received network interaction data, store the receivednetwork interaction data in association with the score in a database onthe storage device, and forward, based on the score, the networkinteraction data to an aggregator; and an aggregator communicativelycoupled to each of the plurality of peers, the aggregator comprising anelectronic processor and a storage device, the aggregator beingconfigured to: receive, from the plurality of peers, the networkinteraction data, generate, based on the network interaction data, modelparameters, and generate reports regarding network interactions.
 2. Thesystem of claim 1, wherein the score is determined based on at least oneof the model parameters, network topology, or statistics regardingclicks.
 3. The system of claim 2, wherein the score is determined basedon at least statistics regarding clicks, and wherein the statisticsregarding clicks comprise statistics regarding at least one ofinterclick time or interclick distributions.
 4. The system of claim 1,wherein the score is determined based on at least an internet protocoladdress.
 5. The system of claim 1, wherein at least one of the pluralityof peers comprises at least one model that is different from the modelsused by the other peers.
 6. The system of claim 5, wherein theaggregator is configured to provide the model parameters to each of theplurality of peers.
 7. The system of claim 1, wherein the aggregatorcomprises a model server configured to generate, based on the networkinteraction data, model parameters and to develop the one or moremodels.
 8. The system of claim 7, wherein the aggregator is furtherconfigured to provide the model parameters to the model server.
 9. Thesystem of claim 1, wherein the aggregator is further configured toobtain a score for the network interaction data based on a model havingthe model parameters.
 10. The system of claim 1, wherein the networkinteraction data comprises an internet protocol address and a timestamp.11. A method for acquiring and aggregating data for network fraudulencedetection, the method comprising: receiving, by peer hardware of each ofa plurality of peers, network interaction data, wherein each peer of theplurality of peers comprises an electronic processor and a storagedevice; determining, by each peer of the plurality of peers and based onone or more models, a respective score for respective networkinteraction data; storing, by each of the plurality of peers, respectivereceived network interaction data in association with a respective scoreon a respective storage device; forwarding, based on a respective score,by each of the plurality of peers and to an aggregator communicativelycoupled to each of the plurality of peers, the network interaction data;generating, by the aggregator and based on the network interaction data,model parameters, and generating, by the aggregator, reports regardingnetwork interactions.
 12. The method of claim 11, wherein thedetermining a respective score comprises determining based on at leastone of the model parameters, network topology, or statistics regardingclicks.
 13. The method of claim 12, wherein the determining a respectivescore comprises determining based on at least statistics regardingclicks, wherein the statistics regarding clicks comprise statisticsregarding at least one of interclick time or interclick distributions.14. The method of claim 11, wherein the determining a respective scorecomprises determining based on at least an internet protocol address.15. The method of claim 11, wherein at least one of the plurality ofpeers comprises at least one model that is different from the modelsused by the other peers.
 16. The method of claim 15, further comprisingproviding, by the aggregator and to each of the plurality of peers, themodel parameters.
 17. The method of claim 11, wherein the aggregatorcomprises a model server, the method further comprising generating, bythe model server and based on the network interaction data, modelparameters; and developing, by the model server, the one or more models.18. The method of claim 17, further comprising providing, by theaggregator and to the model server, the model parameters.
 19. The methodof claim 11, further comprising obtaining, by the aggregator, a scorefor the network interaction data based on a model having the modelparameters.
 20. The method of claim 11, wherein the network interactiondata comprises an internet protocol address and a timestamp.